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As with many buzzwords, it's tempting to think that big data is a little overblown, but if anything, I'm inclined to think the opposite is true, especially as we get better at making sense of the huge amounts of information we have available to us.

Advances in natural language processing has enabled a number of services to take on predictive capabilities to not only make sense of big data, but use it to forecast how things will unfold in the future.

In the past few months, for instance, there have been the following projects unveiled:

• A predictive model by UCLA researchers to predict where crimes will be committed (and therefore where police should be placed)

• A neural network that can predict the price of oil developed by a team from the Middle East

• A platform to use predictive analytics to provide businesses with what it calls 'event intelligence,' which is basically insight into how upcoming events might influence them

• An algorithm that aims to predict the probability of success for a startup

And I'm sure many more. What they all have in common is that they're trying to make sense of huge amounts of data using computers to do what would be beyond us humans.

It's something that Canadian tech company OpenText have as their bread and butter, and to showcase their latest Suite 16 platform they have launched an election tracker service that utilizes a lot of the technology behind the platform.

The platform trawls over 200 carefully chosen sources online for any election orientated coverage before analyzing the content and providing a sentiment analysis of what it finds. The predictive capabilities of the platform aren't turned on yet, but OpenText CEO Mark Barrenechea told me that it could well be made available to the public in time for the presidential elections, and potentially even the British EU referendum later this year.

"No human can read hundreds of newspapers or news sources a day, let alone provide a cognitive, sentiment and general analysis of the information contained in those news sources. But a machine can," Mark J. Barrenechea, CEO of OpenText told me recently.

"OpenText's U.S. Election Tracker reads, analyzes and visualizes key U.S. Election big data, every day, from hundreds of news sources, helping the U.S. voters make more informed decisions. The analysis includes natural language processing, semantic processing, sentiment analysis and opinion algorithms," he continued.

Whilst the Election Tracker is intended as a bit of fun, it and the examples highlighted at the start of this post show the direction we're going in and the potential for incredible insights to be gleaned from the huge amount of data that is available to us.

As the amount of data we have available in areas such as public health, genomics and connected devices goes up, our ability to derive meaningful and rapid insights from that data will become increasingly important.